{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "transform = transforms.Compose(\n",
    "    [transforms.ToTensor(),\n",
    "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "\n",
    "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
    "                                        download=True, transform=transform)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,\n",
    "                                          shuffle=True, num_workers=2)\n",
    "\n",
    "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
    "                                       download=True, transform=transform)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=4,\n",
    "                                         shuffle=False, num_workers=2)\n",
    "\n",
    "classes = ('plane', 'car', 'bird', 'cat',\n",
    "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ship  bird   car  deer\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# functions to show an image\n",
    "\n",
    "\n",
    "def imshow(img):\n",
    "    img = img / 2 + 0.5     # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "\n",
    "\n",
    "# get some random training images\n",
    "dataiter = iter(trainloader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# show images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "# print labels\n",
    "print(' '.join('%5s' % classes[labels[j]] for j in range(4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.autograd import Variable\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
    "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
    "        self.fc2 = nn.Linear(120, 84)\n",
    "        self.fc3 = nn.Linear(84, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.pool(F.relu(self.conv1(x)))\n",
    "        x = self.pool(F.relu(self.conv2(x)))\n",
    "        x = x.view(-1, 16 * 5 * 5)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "net = Net().cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1,  2000] loss: 2.184\n",
      "[1,  4000] loss: 1.879\n",
      "[1,  6000] loss: 1.645\n",
      "[1,  8000] loss: 1.569\n",
      "[1, 10000] loss: 1.519\n",
      "[1, 12000] loss: 1.446\n",
      "[2,  2000] loss: 1.391\n",
      "[2,  4000] loss: 1.370\n",
      "[2,  6000] loss: 1.342\n",
      "[2,  8000] loss: 1.313\n",
      "[2, 10000] loss: 1.315\n",
      "[2, 12000] loss: 1.290\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(2):  # loop over the dataset multiple times\n",
    "\n",
    "    running_loss = 0.0\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        # get the inputs\n",
    "        inputs, labels = data\n",
    "\n",
    "        # wrap them in Variable\n",
    "        inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())\n",
    "\n",
    "        # zero the parameter gradients\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # forward + backward + optimize\n",
    "        outputs = net(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # print statistics\n",
    "        running_loss += loss.data\n",
    "        if i % 2000 == 1999:    # print every 2000 mini-batches\n",
    "            print('[%d, %5d] loss: %.3f' %\n",
    "                  (epoch + 1, i + 1, running_loss / 2000))\n",
    "            running_loss = 0.0\n",
    "\n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'tensor(0.7256)'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss.data.cpu().float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
